Change name to huber loss

test=develop

paddle/fluid/API.spec

@@ -199,7 +199,7 @@ paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None
 paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
 paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.huber_regression_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.huber_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)

python/paddle/fluid/layers/nn.py

@@ -174,7 +174,7 @@ __all__ = [
     'get_tensor_from_selected_rows',
     'lstm',
     'psroi_pool',
-    'huber_regression_loss',
+    'huber_loss',
 ]
 
 kIgnoreIndex = -100
@@ -9180,7 +9180,7 @@ def psroi_pool(input,
     return out
 
 
-def huber_regression_loss(input, label, delta):
+def huber_loss(input, label, delta):
     """
     Huber regression loss is a loss function used in robust regression.
     Huber regression loss can evaluate the fitness of input to label.
@@ -9212,9 +9212,9 @@ def huber_regression_loss(input, label, delta):
         .. code-block:: python
 
             predictions = fluid.layers.softmax(x)
-            loss = fluid.layers.huber_regression_loss(input=predictions, label=label, 1.0)
+            loss = fluid.layers.huber_loss(input=predictions, label=label, 1.0)
     """
-    helper = LayerHelper('huber_regression_loss', **locals())
+    helper = LayerHelper('huber_loss', **locals())
     residual = helper.create_variable_for_type_inference(
         dtype=helper.input_dtype())
     out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())